Source code for stk.molecular.topology_graphs.topology_graph.topology_graph.topology_graph

Topology Graph


from __future__ import annotations

import typing
from collections import abc
from functools import partial

import numpy as np

from stk.utilities import flatten

from ....molecules import BuildingBlock
from ....reactions import ReactionFactory
from ..construction_result import ConstructionResult
from ..construction_state import ConstructionState
from ..edge import Edge
from ..edge_group import EdgeGroup
from ..optimizers import Optimizer
from ..vertex import Vertex
from .implementations import (

_TopologyGraphT = typing.TypeVar(

[docs]class TopologyGraph: """ An abstract base class for topology graphs. It is responsible for the construction of molecules. To create a new topology graph, you want to subclass and implement this abstract base class. Notes: *Adding New Topology Graphs* You might notice that some of the methods of this abstract base class are implemented. This is purely for convenience when implementing subclasses. The implemented public methods are simply default implementations, which can safely be ignored or overridden, when implementing subclasses. Any private methods are implementation details of these default implementations. Many classes, such as :class:`.Vertex`, :class:`.Edge`, :class:`.EdgeGroup` and :class:`.ConstructionState`, exist as implementation details of this default :class:`.TopologyGraph` implementation. You could ignore all of them, and define a new :meth:`.construct` method from scratch. In fact, your topology graph does not have to be represented as a graph at all. However, using the default implementation of :class:`.TopologyGraph` makes it significantly easier to implement a construction process. When using the default implementation of :class:`.TopologyGraph`, you mostly just need to implement a :class:`.Vertex` subclass, which is much easier than figuring out the whole construction process from scratch. In addition, you get benefits like parallel construction for free, as it is included in the default implementation. Typically, adding a new topology graph will involve implementing any pure virtual methods of :class:`.TopologyGraph`, in a new subclass, as well as implementing any pure virtual methods of :class:`.Vertex`, again in a new subclass. Combined, this is just a handful of simple methods to implement. Sometimes, rarely, you might also want to subclass :class:`.ConstructionState`, when you want to add additional hooks during construction, by extending the methods of this class. If you do this, make sure to override :meth:`._get_construction_state` to return your subclass of :class:`.ConstructionState`, rather than the base class, as is done by default. You can subclass and extend the methods of any class as you wish, but it would be unusual if this doesn't cover all your requirements. *The Default Implementation* The default implementation of :class:`.TopologyGraph` represents the constructed molecule through a graph. The vertices indicate where building blocks are placed and the edges indicate which building blocks have bonds formed between them by the construction process. :class:`.Vertex` instances are responsible for placing the building block molecules. By initializing the vertices with different parameters, you can alter how they position the building block molecules, and therefore allow the user to easily specify a different structural isomer. Once a building block is placed on a vertex, the functional groups on the building block must be mapped to the different edges connected to the vertex. The number of functional groups in the building block must match the number of edges connected to the vertex. Once the functional groups are mapped to edges, the edges are used to perform reactions on the building blocks. Edges are grouped in an :class:`.EdgeGroup`, and all functional groups present in the edge group are reacted together. Normally, unless you are doing something very exotic, an :class:`.EdgeGroup` will hold just one :class:`.Edge`, and the two functional groups on that edge will be reacted together through a single :class:`.Reaction`. This reaction will normally add the bonds which are required to form the joined-up constructed molecule, but note that it does not have to add any bonds at all. In addition, a :class:`.Reaction` can add and remove atoms from the constructed molecule. Which reaction is selected to join the functional groups depends on the :class:`.ReactionFactory` given to the :class:`.TopologyGraph` during initialization. Once this is done, you have a :class:`.ConstructedMolecule`. Examples: *Subclass Implementation* The source code of subclasses, listed in :mod:`~.topology_graph.topology_graph.topology_graph`, can serve as good examples. *Changing the Building Blocks of a Topology Graph* To change the building blocks used by a topology graph you can use :meth:`.with_building_blocks` to get a clone of the topology graph holding the new building blocks .. testcode:: changing-the-building-blocks-of-a-topology-graph import stk bb1 = stk.BuildingBlock('BrCCBr', [stk.BromoFactory()]) bb2 = stk.BuildingBlock('BrCCCBr', [stk.BromoFactory()]) linear = stk.polymer.Linear( building_blocks=(bb1, bb2), repeating_unit='A', num_repeating_units=15, ) bb3 = stk.BuildingBlock('BrCNCBr', [stk.BromoFactory()]) # All bb1 instances are replaced by bb3, but bb2 remains # in place. clone = linear.with_building_blocks({ bb1: bb3, }) """ _implementation: _TopologyGraphImplementation
[docs] def __init__( self, building_block_vertices: dict[ BuildingBlock, abc.Sequence[Vertex], ], edges: tuple[Edge, ...], reaction_factory: ReactionFactory, construction_stages: tuple[ # TODO: Use typing.Callable here for now so that Sphinx # generates hyperlinks in the compiled docs. This should # eventually be replaced by abc.Callable. typing.Callable[[Vertex], bool], ..., ], num_processes: int, optimizer: Optimizer, edge_groups: typing.Optional[tuple[EdgeGroup, ...]] = None, ) -> None: """ Initialize an instance of :class:`.TopologyGraph`. Parameters: building_block_vertices: Maps each :class:`.BuildingBlock` to be placed, to the :class:`.Vertex` instances, on which it should be placed. edges: The edges which make up the topology graph. reaction_factory: Used to pick which :class:`.Reaction` is used on each :class:`.EdgeGroup` of the topology graph. construction_stages: A collection of :class:``, each of which takes a :class:`.Vertex` and returns ``True`` or ``False``. If the first :class:`` is applied to a vertex in the topology graph, and the result is ``True``, that vertex is a part of the first construction stage. The second :class:`callable` is then applied to all vertices not in the first stage and those which return ``True`` belong to the second stage and so on. Vertices which belong to the same construction stage all place building blocks together in parallel, before placement is done by any vertices which are part of a later stage. This breaks down parallel construction into serial stages if synchronization between stages is needed. If the topology graph is performing construction serially, then all vertices which belong to an earlier stage will place their building block before those at a later stage. num_processes: The number of parallel processes to create during :meth:`construct`. optimizer: Used to optimize the structure of the constructed molecule. edge_groups: The edge groups of the topology graph, if ``None``, every :class:`.Edge` is in its own edge group. """ self._scale = scale = self._get_scale(building_block_vertices) def apply_scale(item): return item.with_scale(scale) self._building_block_vertices = { building_block: tuple(map(apply_scale, vertices)) for building_block, vertices in building_block_vertices.items() } self._edges = tuple(map(apply_scale, edges)) self._reaction_factory = reaction_factory if num_processes == 1: self._implementation = _Serial( stages=tuple(self._get_stages(construction_stages)), ) else: self._implementation = _Parallel( stages=tuple(self._get_stages(construction_stages)), num_processes=num_processes, ) if edge_groups is None: edge_groups = tuple( EdgeGroup((edge,)) for edge in self._edges ) self._edge_groups = edge_groups self._optimizer = optimizer
def _with_building_blocks( self: _TopologyGraphT, building_block_map: dict[BuildingBlock, BuildingBlock], ) -> _TopologyGraphT: """ Modify the topology graph. """ # The original scaling first needs to be removed, so that when # the scale is recalculated with the new building blocks, it # has the same starting geometry. def undo_scale(vertex): return vertex.with_scale(1 / self._scale) building_block_vertices: dict[ BuildingBlock, abc.Sequence[Vertex] ] building_block_vertices = { building_block_map.get( building_block, building_block ): tuple(map(undo_scale, vertices)) for building_block, vertices in self._building_block_vertices.items() } scale = self._get_scale(building_block_vertices) def scale_vertex(vertex): return vertex.with_scale(scale) self._building_block_vertices = { building_block: tuple(map(scale_vertex, vertices)) for building_block, vertices in building_block_vertices.items() } def scale_edge(edge): # Remove the old scale and apply the new one. return edge.with_scale(scale / self._scale) self._edges = edges = tuple(map(scale_edge, self._edges)) def get_new_edge(edge_id): return edges[edge_id] self._edge_groups = tuple( EdgeGroup(map(get_new_edge, edge_group.get_edge_ids())) for edge_group in self._edge_groups ) self._scale = scale return self
[docs] def with_building_blocks( self, building_block_map: dict[BuildingBlock, BuildingBlock], ) -> TopologyGraph: """ Return a clone holding different building blocks. Parameters: building_block_map: Maps a building block in the current topology graph to the building block which should replace it in the clone. If a building block should be not replaced in the clone, it can be omitted from the map. Returns: The clone. """ return self.clone()._with_building_blocks(building_block_map)
[docs] def clone(self) -> TopologyGraph: """ Return a clone. Returns: The clone. """ return self._clone()
def _clone(self: _TopologyGraphT) -> _TopologyGraphT: clone = self.__class__.__new__(self.__class__) clone._scale = self._scale clone._building_block_vertices = dict( self._building_block_vertices ) clone._edges = self._edges clone._reaction_factory = self._reaction_factory clone._implementation = self._implementation clone._optimizer = self._optimizer clone._edge_groups = self._edge_groups return clone
[docs] def get_building_blocks(self) -> typing.Iterator[BuildingBlock]: """ Yield the building blocks. Building blocks are yielded in an order based on their position in the topology graph. For two equivalent topology graphs, but with different building blocks, equivalently positioned building blocks will be yielded at the same time. Yields: A building block of the topology graph. """ vertex_building_blocks = {} num_vertices = 0 for ( building_block, vertices, ) in self._building_block_vertices.items(): for vertex in vertices: num_vertices += 1 vertex_building_blocks[ vertex.get_id() ] = building_block yielded = set() for vertex_id in range(num_vertices): building_block = vertex_building_blocks[vertex_id] if building_block not in yielded: yielded.add(building_block) yield building_block
[docs] def get_num_building_block( self, building_block: BuildingBlock, ) -> int: """ Get the number of times `building_block` is present. Parameters: building_block: The building block whose frequency in the topology graph is desired. Returns: The number of times `building_block` is present in the topology graph. """ return len( self._building_block_vertices.get(building_block, []) )
def _get_lattice_constants(self) -> typing.Iterator[np.ndarray]: """ Yield the lattice constants of the topology graph. The a, b and c lattice constants are yielded, in that order. By default, this is an empty generator. Yields: A lattice constant. """ return yield
[docs] def construct(self) -> ConstructionResult: """ Construct a :class:`.ConstructedMolecule`. Returns: The data describing the :class:`.ConstructedMolecule`. """ state = self._get_construction_state() state = self._place_building_blocks(state) state = self._run_reactions(state) state = self._optimizer.optimize(state) return self._get_construction_result(state)
def _get_construction_result( self, state: ConstructionState, ) -> ConstructionResult: """ Get the result of the construction. Parameters: state: The state of the molecule being constructed. Returns: The data describing the :class:`.ConstructedMolecule`. """ return ConstructionResult(state) def _get_construction_state(self) -> ConstructionState: return ConstructionState( building_block_vertices=self._building_block_vertices, edges=self._edges, lattice_constants=tuple( np.array(constant, dtype=np.float64) * self._scale for constant in self._get_lattice_constants() ), ) def _get_scale( self, building_block_vertices: dict[ BuildingBlock, abc.Sequence[Vertex], ], ) -> float: """ Get the scale, which should be applied to topology graph. The scale should be applied to the position of every vertex and edge of topology graph. This allows to graph to adjust based on the size of the building blocks. Parameters: building_block_vertices: Maps every :class:`.BuildingBlock` of the topology graph, to the :class:`.Vertex` instances it is meant to be placed on. Returns: The scale. """ raise NotImplementedError() def _place_building_blocks( self, state: ConstructionState, ) -> ConstructionState: """ Place the building blocks onto the vertices. Parameters: state: Holds data necessary to construct the molecule. Returns: The new construction state, updated to account for the placed building blocks. """ return self._implementation._place_building_blocks(state) def _run_reactions( self, state: ConstructionState, ) -> ConstructionState: """ Perform the reactions on the building blocks. Parameters: state: The current state of the construction process. Returns: The new construction state, updated to account for the reactions between building blocks. """ get_reaction = partial( self._reaction_factory.get_reaction, state, ) reactions = tuple(map(get_reaction, self._edge_groups)) results = map( lambda reaction: reaction.get_result(), reactions, ) return state.with_reaction_results(reactions, results) def _get_stages( self, construction_stages, ) -> typing.Iterator[tuple[int, ...]]: """ Yield the parallelizable stages of construction. Yields: Vertices ids, which can be placed in parallel. """ stages: tuple[list[int], ...] = tuple( [] for i in range(len(construction_stages) + 1) ) vertices = flatten(self._building_block_vertices.values()) for vertex in vertices: placed = False for i, stage in enumerate(construction_stages): if stage(vertex): stages[i].append(vertex.get_id()) placed = True break if not placed: stages[-1].append(vertex.get_id()) yield from (tuple(stage) for stage in stages if stage) def __str__(self) -> str: return repr(self) def __repr__(self) -> str: raise NotImplementedError()